C2GMatch: Leveraging Dual-View Cross-Guidance and Co-Guidance Framework for Semi-Supervised Cell Segmentation

Automated white blood cell segmentation is crucial for diagnosing and monitoring severe conditions like leukemia and lymphoma. However, this task is hindered by the scarcity and quality of available public datasets. To make better use of these limited datasets, a semi-supervised learning framework is required. In our study, we introduce the C2GMatch framework, which leverages weak-to-strong dual-view guidance to enhance segmentation performance from partially labeled data. Our approach surpasses two out of three leading semi-supervised segmentation methods on three public datasets, including BCCD, LISC, and LiveCells, achieving IOU scores of 54.12% and 68.13% and Dice scores of 69.97% and 82.01% on LISC and LiveCells, respectively. Our framework also shows better segmentation masks compared to previous works such as Pseudo-Seg or Unimatch in qualitative results.